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Schizophrenia is associated with alterations of sensory integration, cognitive processing and both sleep architecture and sleep oscillations in mouse models and human subjects, possibly through changes in thalamocortical dynamics. Oxidative stress (OxS) damage, including inflammation and the impairment of fast-spiking gamma-aminobutyric acid neurons have been hypothesized as a potential mechanism responsible for the onset and development of schizophrenia. Yet, the link between OxS and perturbation of thalamocortical dynamics and sleep remains unclear. Here, we sought to investigate the effects of OxS on sleep regulation by characterizing the dynamics of thalamocortical networks across sleep-wake states in a mouse model with a genetic deletion of the modifier subunit of glutamate-cysteine ligase (Gclm knockout, KO) using high-density electrophysiology in freely-moving mice. We found that Gcml KO mice exhibited a fragmented sleep architecture and impaired sleep homeostasis responses as revealed by the increased NREM sleep latencies, decreased slow-wave activities and spindle rate after sleep deprivation. These changes were associated with altered bursting activity and firing dynamics of neurons from the thalamic reticularis nucleus, anterior cingulate and anterodorsal thalamus. Administration of N-acetylcysteine (NAC), a clinically relevant antioxidant, rescued the sleep fragmentation and spindle rate through a renormalization of local neuronal dynamics in Gclm KO mice. Collectively, these findings provide novel evidence for a link between OxS and the deficits of frontal TC network dynamics as a possible mechanism underlying sleep abnormalities and impaired homeostatic responses observed in schizophrenia.

Sleep has been conceptualized as ‘activity-dependent’, hence a response to prior waking experience, and proposed to be ‘the price the brain pays for plasticity during wakefulness’. We here propose that at the level of neuronal networks, particularly those arising from isolated embryonic thalamocortical cells maintained in culture, it represents a default mode of functioning. We show that cell assemblies in ex vivo cultures express powerful sleep specific patterns of oscillatory activity, as well as metabolic and molecular signatures of the sleep state. We summarize recent evidences that support our hypothesis and discuss potential applications of developing ex vivo sleep models to answer open questions in the field.

Hypocretin (Hcrt), also known as orexin, neuropeptide signaling stabilizes sleep and wakefulness in all vertebrates. A lack of Hcrt causes the sleep disorder narcolepsy, and increased Hcrt signaling has been speculated to cause insomnia, but while the signaling pathways of Hcrt are relatively well-described, the intracellular mechanisms that regulate its expression remain unclear. Here, we tested the role of microRNAs (miRNAs) in regulating Hcrt expression. We found that miR-137, miR-637, and miR-654-5p target the human HCRT gene. miR-137 is evolutionarily conserved and also targets mouse Hcrt as does miR-665. Inhibition of miR-137 specifically in Hcrt neurons resulted in Hcrt upregulation, longer episodes of wakefulness, and significantly longer wake bouts in the first 4 h of the active phase. IL-13 stimulation upregulated endogenous miR-137, while Hcrt mRNA decreased both in vitro and in vivo. Furthermore, knockdown of miR-137 in zebrafish substantially increased wakefulness. Finally, we show that in humans, the MIR137 locus is genetically associated with sleep duration. In conclusion, these results show that an evolutionarily conserved miR-137:Hcrt interaction is involved in sleep–wake regulation.

Both dopaminergic (DA) and orexinergic (OX) systems establish brain-wide neuromodulatory circuits that profoundly influence brain states and behavioral outputs. To unravel their interactions, we inactivated OX-to-DA neurotransmission by selective disruption of HcrtR1/OxR1, or HcrtR2/OxR2, or both receptors, in DA neurons. Chronic loss of OXR2 in DA neurons (OxR2Dat-CKO mice) dramatically increased electrocorticographic (EcoG) theta rhythms in wakefulness and REM sleep. Episode duration and total times spent in ‘active’ wakefulness and REMS were prolonged, and theta/fast-gamma wave coupling was enhanced in both states. Increased theta in OxR2DatCKO mice baseline wake was accompanied by diminished infra-theta and increased fast-gamma activities, i.e. the mice exhibited signs of constitutive electrocortical hyperarousal, albeit uncoupled with locomotor activity. These effects were not seen in OxR1-ablated dopaminergic mutants, which tended to show opposite phenotypes, resembling those caused by the loss of both receptors. Our data establish a clear, genetically-defined link between monosynaptic orexin-to-dopaminergic connectivity and the power of theta oscillations, with a differential role of OXR2 in cross-frequency wave coupling and attentional processes.

Functional recovery after stroke is associated with a remapping of neural circuits. This reorganization is often associated with low-frequency, high-amplitude oscillations in the peri-infarct zone in both rodents and humans. These oscillations are reminiscent of sleep slow waves (SW) and suggestive of a role for sleep in brain plasticity that occur during stroke recovery; however, direct evidence is missing. Using a stroke model in male mice, we showed that stroke was followed by a transient increase in NREM sleep accompanied by reduced amplitude and slope of ipsilateral NREM sleep SW. We next used 5 ms optical activation of Channelrhodopsin 2-expressing pyramidal neurons, or 200 ms silencing of Archeorhodopsin T-expressing pyramidal neurons, to generate local cortical UP, or DOWN, states, respectively, both sharing similarities with spontaneous NREM SW in freely moving mice. Importantly, we found that single optogenetically evoked SW (SWopto) in the peri-infarct zone, randomly distributed during sleep, significantly improved fine motor movements of the limb corresponding to the sensorimotor stroke lesion site compared with spontaneous recovery and control conditions, while motor strength remained unchanged. In contrast, SWoptoduring wakefulness had no effect. Furthermore, chronic SWoptoduring sleep were associated with local axonal sprouting as revealed by the increase of anatomic presynaptic and postsynaptic markers in the peri-infarct zone and corresponding contralesional areas to cortical circuit reorganization during stroke recovery. These results support a role for sleep SW in cortical circuit plasticity and sensorimotor recovery after stroke and provide a clinically relevant framework for rehabilitation strategies using neuromodulation during sleep.SIGNIFICANCE STATEMENTBrain stroke is one of the leading causes of death and major disabilities in the elderly worldwide. A better understanding of the pathophysiological mechanisms underlying spontaneous brain plasticity after stroke, together with an optimization of rehabilitative strategies, are essential to improve stroke treatments. Here, we investigate the role of optogenetically induced sleep slow waves in an animal model of ischemic stroke and identify sleep as a window for poststroke intervention that promotes neuroplasticity and facilitates sensorimotor recovery.

A growing body of evidence implicates thalamo-cortical oscillations with the neuropathophysiology of schizophrenia (SZ) in both mice and humans. Yet, the precise mechanisms underlying sleep perturbations in SZ remain unclear. Here, we characterised the dynamics of thalamo-cortical networks across sleep-wake states in a mouse model carrying a mutation in the enzyme glutathione synthetase gene (Gclm-/-) associated with SZ in humans. We hypothesised that deficits in parvalbumin immunoreactive cells in the thalamic reticular nucleus (TRN) and the anterior cingulate cortex (ACC) - caused by oxidative stress - impact thalamocortical dynamics, thus affecting non-rapid eye movement (NREM) sleep and sleep homeostasis. Using polysomnographic recordings in mice, we showed that KO mice exhibited a fragmented sleep architecture, similar to SZ patients and altered sleep homeostasis responses revealed by an increase in NREM latency and slow wave activities during the recovery period (SR). Although NREM sleep spindle rate during spontaneous sleep was similar in Gclm-/- and Gcml +/+, KO mice lacked a proper homeostatic response during SR. Interestingly, using multisite electrophysiological recordings in freely-moving mice, we found that high order thalamic network dynamics showed increased synchronisation, that was exacerbated during the sleep recovery period subsequent to SD, possibly due to lower bursting activity in TRN-antero dorsal thalamus circuit in KO compared to WT littermates. Collectively, these findings provide a mechanism for SZ associated deficits of thalamo-cortical neuron dynamics and perturbations of sleep architecture.

Significance The lateral hypothalamus encompasses neural circuits that are highly active during feeding behavior and rapid eye movement sleep. Yet, it remains unclear whether these mutually exclusive behaviors share common neural populations. Here, we recorded and perturbed the activity of inhibitory neurons of the lateral hypothalamus (LH) across feeding behavior and sleep in freely behaving mice. We found that feeding is reliably encoded by specific patterns of neuron activity and that these patterns are reactivated during rapid eye movement sleep. Disrupting the activity of these inhibitory neurons specifically during rapid eye movement sleep decreased subsequent feeding behavior. These results suggest that rapid eye movement sleep stabilizes the hypothalamic representation of feeding behavior and modulates future food intake., During rapid eye movement (REM) sleep, behavioral unresponsiveness contrasts strongly with intense brain-wide neural network dynamics. Yet, the physiological functions of this cellular activation remain unclear. Using in vivo calcium imaging in freely behaving mice, we found that inhibitory neurons in the lateral hypothalamus (LHvgat) show unique activity patterns during feeding that are reactivated during REM, but not non-REM, sleep. REM sleep-specific optogenetic silencing of LHvgat cells induced a reorganization of these activity patterns during subsequent feeding behaviors accompanied by decreased food intake. Our findings provide evidence for a role for REM sleep in the maintenance of cellular representations of feeding behavior.

Prader–Willi syndrome (PWS) is a neurodevelopmental disorder that is characterized by metabolic alteration and sleep abnormalities mostly related to rapid eye movement (REM) sleep disturbances. The disease is caused by genomic imprinting defects that are inherited through the paternal line. Among the genes located in the PWS region on chromosome 15 (15q11-q13), small nucleolar RNA 116 (Snord116) has been previously associated with intrusions of REM sleep into wakefulness in humans and mice. Here, we further explore sleep regulation of PWS by reporting a study with PWScrm+/p− mouse line, which carries a paternal deletion of Snord116. We focused our study on both macrostructural electrophysiological components of sleep, distributed among REMs and nonrapid eye movements. Of note, here, we study a novel electroencephalography (EEG) graphoelements of sleep for mouse studies, the well-known spindles. EEG biomarkers are often linked to the functional properties of cortical neurons and can be instrumental in translational studies. Thus, to better understand specific properties, we isolated and characterized the intrinsic activity of cortical neurons using in vitro microelectrode array. Our results confirm that the loss of Snord116 gene in mice influences specific properties of REM sleep, such as theta rhythms and, for the first time, the organization of REM episodes throughout sleep–wake cycles. Moreover, the analysis of sleep spindles present novel specific phenotype in PWS mice, indicating that a new catalog of sleep biomarkers can be informative in preclinical studies of PWS.

Prader-Willi syndrome (PWS) is a neurodevelopmental disorder that is characterized by rapid eye movement (REM) sleep abnormalities. The disease is caused by genomic imprinting defects that are inherited through the paternal line. Among the genes located in the PWS region on chromosome 15 (15q11-q13), small nucleolar RNA 116 (Snord116) has been previously associated with intrusions of REM sleep into wakefulness in both humans and mice.Here, we further explore the processes of sleep regulation by studying the PWScrm+/p-mouse line, which carries a paternal deletion ofSnord116.We focused on microstructural electrophysiological components of sleep, such as REM sleep features and sleep spindles within NREM sleep. While the former are thought to contribute to neuronal network formation early in brain development, the latter are markers of thalamocortical processes. Both signals are often compromised in neurodevelopmental disorders and influence functional properties of cortical neurons. Thus, we isolated and characterized the intrinsic activity of cortical neurons usingin vitromicroelectrode array (MEA) studies.Our results indicate that theSnord116gene in mice selectively influences REM sleep properties, such as theta rhythms and the organization of REM episodes throughout sleep-wake cycles. Moreover, sleep spindles present specific abnormalities in PWS model systems, indicating that these features of sleep may translate as potential biomarkers in human PWS. We observed abnormalities in the synchronization of cortical neuronal activity that are accounted for by high levels of norepinephrine.In conclusion, our results provide support for an important role ofSnord116in regulating brain activity during sleep and, in particular, cortical neuronal properties, thereby opening new avenues for developing interventions in PWS.Significance StatementWe found that theSnord116gene, a major player in Prader-Willi syndrome (PWS), significantly impacts REM sleep and its regulation. Additionally, we found that sleep spindles, a subtle electroencephalography (EEG) marker that occurs during NREM sleep, are dysregulated in PWS mice that carry a paternal deletion of theSnord116gene. Using a combination ofin vivoandin vitroexperiments, we identified sleep features at the network and molecular level that suggest thatSnord116is fundamental in the synchronization of neuronal networks. Our study also provides a new pre-clinical tool to investigate the pathophysiology of sleep in PWS.

Theta phase modulates gamma amplitude in hippocampal networks during spatial navigation and rapid eye movement (REM) sleep. This cross-frequency coupling has been linked to working memory and spatial memory consolidation; however, its spatial and temporal dynamics remains unclear. Here, we first investigate the dynamics of theta–gamma interactions using multiple frequency and temporal scales in simultaneous recordings from hippocampal CA3, CA1, subiculum, and parietal cortex in freely moving mice. We found that theta phase dynamically modulates distinct gamma bands during REM sleep. Interestingly, we further show that theta–gamma coupling switches between recorded brain structures during REM sleep and progressively increases over a single REM sleep episode. Finally, we show that optogenetic silencing of septohippocampal GABAergic projections significantly impedes both theta–gamma coupling and theta phase coherence. Collectively, our study shows that phase-space (i.e. cross-frequency coupling) coding of information during REM sleep is orchestrated across time and space consistent with region-specific processing of information during REM sleep including learning and memory.

Objective Prediction of epileptic seizures can improve the living conditions for refractory epilepsy patients. We aimed to improve sensitivity and specificity of prediction methods, and to reduce the number of false alarms. Methods Relative combinations of sub-band spectral powers of electroencephalogram (EEG) recordings across all possible channel pairs were utilized for tracking gradual changes preceding seizures. By using a specifically developed feature selection method, a set of best candidate features were fed to support vector machines in order to discriminate cerebral state as preictal or non-preictal. Results Proposed algorithm was evaluated on continuous long-term multichannel scalp and invasive recordings (183 seizures, 3565h). The best results demonstrated a sensitivity of 75.8% (66 out of 87 seizures) and a false prediction rate of 0.1h − 1 . Performance was validated statistically, and was superior to that of analytical random predictor. Conclusion Applying machine learning methods on a reduced subset of proposed features could predict seizure onsets with high performance. Significance Our method was evaluated on long-term continuous recordings of overall about 5months, contrary to majority of previous studies using short-term fragmented data. It is of very low computational cost, while providing acceptable levels of alarm sensitivity and specificity.

Theta phase modulates gamma amplitude during spatial navigation and rapid eye movement sleep (REMs). Although the REMs theta rhythm has been linked to spatial memory consolidation, the underlying mechanism remains unclear. We investigate dynamics of theta-gamma interactions across multiple frequency and temporal scales in simultaneous recordings from hippocampal CA3, CA1, subiculum, and parietal cortex. We show that theta phase significantly modulates three distinct gamma bands during REMs, dynamically. Interestingly, we further show that theta-gamma coupling swings between different hippocampal and cortical structures during REMs and tends to increase over a single REMs episode. Comparing to active wake, theta-gamma coupling during REMs is significantly increased for subicular and cortical, but not for CA3 and CA1, recordings. Finally, optogenetic silencing of septohippocampal GABAergic projections significantly impedes both theta-gamma coupling and theta phase coherence, two neural mechanisms of working and long-term memory, respectively. Thus, we show that theta-gamma coupling and theta phase coherence are highly modulated during single REMs episode and propose that theta-gamma coupling provides a predominant mechanism for information processing within each brain region, while the orchestrated nature of coupling activity establishes a specific phase-space coding of information during sleep.

Objective Outcome prognostication in comatose patients after cardiac arrest (CA) remains a major challenge. Here we investigated the prognostic value of combinations of linear and non-linear bivariate EEG synchronization measures. Methods 94 comatose patients with EEG within 24 h after CA were included. Clinical outcome was assessed at 3 months using the Cerebral Performance Categories (CPC). EEG synchronization between the left and right parasagittal, and between the frontal and parietal brain regions was assessed with 4 different quantitative measures (delta power asymmetry, cross-correlation, mutual information, and transfer entropy). 2/3 of patients were used to assess the predictive power of all possible combinations of these eight features (4 measures × 2 directions) using cross-validation. The predictive power of the best combination was tested on the remaining 1/3 of patients. Results The best combination for prognostication consisted of 4 of the 8 features, and contained linear and non-linear measures. Predictive power for poor outcome (CPC 3–5), measured with the area under the ROC curve, was 0.84 during cross-validation, and 0.81 on the test set. At specificity of 1.0 the sensitivity was 0.54, and the accuracy 0.81. Conclusion Combinations of EEG synchronization measures can contribute to early prognostication after CA. In particular, combining linear and non-linear measures is important for good predictive power. Significance Quantitative methods might increase the prognostic yield of currently used multi-modal approaches.

The main objective of the research study described herein is to build two analytical models based on artificial neural networks (ANNs) and the statistical mixture design (SMD) method to predict the required performance of ultra-high-performance concrete (UHPC). Two different curing conditions—heat treatment and water storage—were applied to the specimens. To train the neural network, a total set of 53 different mixtures was designed based on the design matrix of SMD. The statistical analysis results showed the adequacy of both models to predict the required performance of UHPC; however, the ANN model could predict the compressive strength (water storage) and slump flow with higher accuracy than the SMD. The optimum combination of the cement, silica fume, and quartz flour was determined to be 24, 9, and 5% by total volume to achieve a flowable mixture with the highest compressive strength. The accuracy of the model was verified with additional experimental tests.

A novel approach using neuronal potential similarity (NPS) of two intracranial electroencephalogram (iEEG) electrodes placed over the foci is proposed for automated early seizure detection in patients with refractory partial epilepsy. The NPS measure is obtained from the spectral analysis of space-differential iEEG signals. Ratio between the NPS values obtained from two specific frequency bands is then investigated as a robust generalized measure, and reveals invaluable information about seizure initiation trends. A threshold-based classifier is subsequently applied on the proposed measure to generate alarms. The performance of the method was evaluated using cross-validation on a large clinical dataset, involving 183 seizure onsets in 1785 h of long-term continuous iEEG recordings of 11 patients. On average, the results show a high sensitivity of 86.9% (159 out of 183), a very low false detection rate of 1.4 per day, and a mean detection latency of 13.1 s from electrographic seizure onsets, while in average preceding clinical onsets by 6.3 s. These high performance results, specifically the short detection latency, coupled with the very low computational cost of the proposed method make it adequate for using in implantable closed-loop seizure suppression systems.

This paper presents two low complexity and yet robust methods for automated seizure detection using a set of 2 intracranial Electroencephalogram (iEEG) recordings. Most current seizure detection methods suffer from high number of false alarms, even when designed to be subject-specific. In this study, the ratios of power between pairs of frequency bands are used as features to detect epileptic seizures. For comparison, these features are calculated from monopolar and bipolar iEEG recordings. Optimal thresholds are individually determined and used for each feature. Alarms are generated when the measure passes the threshold. The detector was applied to long-term continuous invasive recordings from 5 patients with refractory partial epilepsy, containing 54 seizures in 780 hours. On average, the results revealed 88.9% sensitivity, a very low false detection rate of 0.041 per hour (h(-1)) and detection latency of 9.4 seconds.

The problem of multiple surface clustering is a challenging task, particularly when the surfaces intersect. Available methods such as Isomap fail to capture the true shape of the surface near by the intersection and result in incorrect clustering. The Isomap algorithm uses shortest path between points. The main draw back of the shortest path algorithm is due to the lack of curvature constrained where causes to have a path between points on different surfaces. In this paper we tackle this problem by imposing a curvature constraint to the shortest path algorithm used in Isomap. The algorithm chooses several landmark nodes at random and then checks whether there is a curvature constrained path between each landmark node and every other node in the neighborhood graph. We build a binary feature vector for each point where each entry represents the connectivity of that point to a particular landmark. Then the binary feature vectors could be used as a input of conventional clustering algorithm such as hierarchical clustering. We apply our method to simulated and some real datasets and show, it performs comparably to the best methods such as K-manifold and spectral multi-manifold clustering.

Supervised machine learning-based seizure prediction methods consider preictal period as an important prerequisite parameter during training. However, the exact length of the preictal state is unclear and varies from seizure to seizure. We propose a novel statistical approach for proper selection of the preictal period, which can also be considered either as a measure of predictability of a seizure or as the prediction capability of an understudy feature. The optimal preictal periods (OPPs) obtained from the training samples can be used for building a more accurate classifier model. The proposed method uses amplitude distribution histograms of features extracted from electroencephalogram (EEG) recordings. To evaluate this method, we extract spectral power features in different frequency bands from monopolar and space-differential EEG signals of 18 patients suffering from pharmacoresistant epilepsy. Furthermore, comparisons among monopolar channels with space-differential channels, as well as intracranial EEG (iEEG) and surface EEG (sEEG) signals, indicate that while monopolar signals perform better in iEEG recordings, no significant difference is noticeable in sEEG recordings.

A new system developed for real-time scalp EEG-based epileptic seizure prediction is presented, based on real time classification by machine learning methods, and named Brainatic. The system enables the consideration of previously trained classifiers for real-time seizure prediction. The software facilitates the computation of 22 univariate measures (features) per electrode, and classification using support vector machines (SVM), multilayer perceptron (MLP) neural networks and radial basis functions (RBF) neural networks. Brainatic was able to operate in real-time on a dual Intel® AtomTM netbook with 2GB of RAM, and was used to perform the clinical and ambulatory tests of the EU project EPILEPSIAE.

This paper presents the results of our study on sub-band phase synchronization of neural activity for automated detection of epileptic seizures. Mean phase coherence (MPC) as a measure of phase synchronization is extracted from the two adjacent intracranial electroencephalogram (iEEG) recordings, which are bandpass-filtered within the desired frequency bands. A threshold-based classifier is applied to generate the alarms. Results can be useful in two ways: for automated seizure detection, and for gaining better understanding of the synchronization behavior of epileptic seizures. The proposed method was applied on 5 invasive recordings selected from the EPILEPSIAE database. The results are compared with the MPC measures extracted from artifact removed raw iEEG signals, and show significant improvement for automated epileptic seizure detection. On average the subband MPC results in a sensitivity of 79% (43 of 54 seizures in 780h recordings) and a false detection rate of 0.05 per hour.

Combining multiple linear univariate features in one feature space and classifying the feature space using machine learning methods could predict epileptic seizures in patients suffering from refractory epilepsy. For each patient, a set of twenty-two linear univariate features were extracted from 6 electroencephalogram (EEG) signals to make a 132 dimensional feature space. Preprocessing and normalization methods of the features, which affect the output of the seizure prediction algorithm, were studied in terms of alarm sensitivity and false prediction rate (FPR). The problem of choosing an optimal preictal time was tackled using 4 distinct values of 10, 20, 30, and 40 min. The seizure prediction problem has traditionally been considered a two-class classification problem, which is also exercised here. These studies have been conducted on the features obtained from 10 patients. For each patient, 48 different combinations of methods are compared to find the best configuration. Normalization by dividing by the maximum and smoothing are found to be the best configuration in most of the patients. The results also indicate that applying machine learning methods on a multidimensional feature space of 22 univariate features predicted seizure onsets with high performance. On average, the seizures were predicted in 73.9% of the cases (34 out of 46 in 737.9 h of test data), with a FPR of 0.15 h −1 .

Two methods for output regularization of support vector machines (SVMs) classifiers were applied for seizure prediction in 10 patients with long-term annotated data. The output of the classifiers were regularized by two methods: one based on the Kalman Filter (KF) and other based on a measure called the "Firing Power" (FP). The FP is a quantification of the rate of the classification in the preictal class in a past time window. In order to enable the application of the KF, the classification problem was subdivided in a two two-class problem, and the real-valued output of SVMs was considered. The results point that the FP method raise less false alarms than the KF approach. However, the KF approach presents an higher sensitivity, but the high number of false alarms turns their applicability negligible in some situations.

This paper presents visual features for tracking of moving object in video sequences using Mean Shift algorithm. The features used in this paper are color, edge and texture. Mean shift Algorithm is expanded based on mentioned multiple features, which are described with highly nonlinear models. In the proposed method, firstly all the features is extracted from first frame and the histogram of each feature is computed then the mean shift algorithm is run for each feature independently and the output of the mean shift algorithm for each feature is weighted based on the similarity measure. In last step, center of the target in the new frame is computed through the integration of the outputs of mean shift. We show that tracking with multiple weighted features provides more reliable performance than single features tracking.

An object tracking algorithm that uses the flexible kernels based on the normalized Metric d α Distance Transform for the Mean shift procedure is proposed and tested. This replaces the more usual Epanechnikov kernel (E-kernel), improving target representation and localization without increasing the processing time, minimizing the similarity measure using the Bhattacharya coefficient. The target shape which defines the d α Distance Transform is found either by regional segmentation or background-difference imaging, dependent on the nature of the video sequence .The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.

A new approach for image classification based on the color information, shape and texture is presented. In this work, we use the three RGB bands of a color image in RGB model to extract the describing features. All the images in image database are divided into 6 parts. We use the Daubechies 4 wavelet transform and first order color moments to obtain the necessary information from each part of the image. The proposed image classification system is based on Back propagation neural network with one hidden layer. Color moments and wavelet decomposition coefficients from each part of the image are used as an input vector of neural network. 150 color images of aircrafts were used for training and 250 for testing. The best efficiency of 98% was obtained for training set, and 90% for the testing set.

In this paper we introduce a new adaptive two-level blocking coordinated checkpointing for cluster computing systems. First level of checkpointing is local checkpointing and computing nodes save the checkpoints in local disk based on transient failure rates. If a transient failure occurs in the computing node, process can recover from local disk. Second level of checkpointing is global checkpointing and computing nodes send their checkpoints to high reliable global stable storage in network based on the expected recovery time in the case of permanent failure. If a permanent failure occurs in the computing node, computing node can not be used and process can recover from global storage in a new computing node. Transient failures are probable than permanent failures and the number of global checkpointingis very lower than local checkpointing. Based on this method, coordinated checkpointing overhead is reduced and it is proportional to transient and permanent failure rates of cluster systems.

In this paper, a new method for contour tracking of mobile target in video sequences is presented. Proposed method helps to track variety of targets exactly while the camera is moving. In this study, a new type of active contour is used with a way for estimating motion model of the target. The novelty of this paper comes from using a new color to gray level transform instead of conventional counterpart. Estimating motion model of the target uses color and location information together. This information helps the proposed method to be robust against the aspect change. Our method is compared with two other methods: tracking via single active contour and estimating motion model by using affine transform. Experimental results illustrate our method to outperform the prior ones.

In this paper we explain a fully automatic system for airplane detection and tracking based on wavelet transform and Support Vector Machine (SVM). By using 50 airplane images in different situations, models are developed to recognize airplane in the first frame of a video sequence. To train a SVM classifier for classifying pixels belong to objects and background pixels, vectors of features are built. The learned model can be used to detect the airplane in the original video and in the novel images. For original video, the system can be considered as a generalized tracker and for novel images it can be interpreted as method for learning models for object recognition. After airplane detection in the first frame, the feature vectors of this frame are used to train the SVM classifier. For new video frame, SVM is applied to test the pixels and form a confidence map. The 4th level of Daubechies's wavelet coefficients corresponding to input image are used as features. Conducting simulations, it is demonstrated that airplane detection and tracking based on wavelet transform and SVM classification result in acceptable and efficient performance. The experimental results agree with the theoretical results.